Bibliographic record
Abstract
Dental implants are being utilized by numerous dentists to replace missing teeth by restoring the function of teeth without compromising the aesthetics. An implant is attached to the bone through the process of osseointegration, which is the connection between bone and artificial implant. For a dental implant to be successful long-term stable osseointegration is required. Early dental implant failure (EDIF) usually occurs within the first four months of implant placement. Osseointegration is dependent on the serum levels of vitamin D. Vitamin D is a secosteroid hormone synthesized by skin cells under the influence of UV radiation or is ingested through a diet or supplemental medication. Low levels of vitamin D negatively affect bone formation thus, affecting the longevity of implant. Vitamin D facilitates bone metabolism, alveolar bone resorption thus, preventing tooth loss. The relationship between bone formation and vitamin D levels have been observed in animal models. According to numerous studies conducted on rodents, vitamin D has been found to increase bone formation around implants. Vitamin D serum levels can be influenced by a variety of factors such as malnutrition, insufficient sun exposure, pigmented skin, obesity and advanced age. As vitamin D levels decrease with increasing age, osteoporosis and periodontal diseases are often diagnosed within the elderly population. Vitamin D is involved in the wound healing process and involves numerous different cells and calcium signaling pathways. This review paper will investigate the relationship between serum vitamin D levels and its impact on wound healing and EDIF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".